Eye movements provide a rich and informative window into a person's
thoughts and intentions. In recent years researchers have
increasingly employed eye movements to study cognition in
psychological experiments, to understand behavior in user interfaces,
and even to control computers through eye-based input devices.
Unfortunately, like speech and handwriting, eye movements generate
vast amounts of data with significant individual variability and
equipment noise. Thus, the analysis of eye-movement data -- that is,
determining what people are thinking based on where they are looking
-- can be extremely tedious and time-consuming. Typical eye-movement
data sets are simply too large and complex to be analyzed by hand or
by naive automated methods.

This thesis formalizes a new class of algorithms that provide fast and
robust analysis of eye-movement data. Specifically, the thesis
describes three novel algorithms for tracing eye movements -- mapping
eye-movement protocols to the sequential predictions of a cognitive
process model. Two algorithms, fixation tracing and point tracing,
employ hidden Markov models to determine the best probabilistic
interpretation of the data given the model. The third algorithm,
target tracing, extends an existing tracing algorithm based on
sequence matching to eye movements. The thesis also formalizes
several algorithms for identifying fixations in raw eye-movement
protocols and provides a working system, EyeTracer, that embodies the
proposed tracing and fixation-identification algorithms.

To demonstrate the power of the proposed algorithms, the thesis
applies them in three real-world domains: equation solving, reading,
and eye typing. The equation-solving studies show how the algorithms
can code, or interpret, eye-movement protocols as accurately as expert
human coders in significantly less time. The studies also illustrate
how the algorithms facilitate the prototyping and refinement of
cognitive models. The reading study demonstrates how the algorithms
help to evaluate and compare two existing computational models of
reading and clear up temporal aspects of reading data using sequential
aspects of the data. The eye-typing study shows how the algorithms
can interpret eye movements in real time and help eliminate usability
restrictions imposed by existing eye-based interfaces.